Evaluation of a tetramine-appended MOF for post-combustion CO2 capture from natural gas combined cycle flue gas by steam-assisted temperature swing adsorption
Bibliographic record
Abstract
A novel tetraamine-appended metal-organic framework (MOF), exhibiting double- stepped isotherm was explored in a 3-step steam-assisted temperature swing adsorption process (SA-TSA) for CO2 removal from dry flue gas emitted from natural gas-fired power plants (NGCC). The reported material exhibited properties highly suited for CO2 capture from dilute sources. Extensive numerical simulations were performed to comprehend the impact of isotherm shape, heat transfer coefficient, feed temperature and heat capacity of solid on adsorption and desorption dynamics in a fixed bed. A multi-objective optimization was performed to identify operating conditions that achieve low steam consumption and high productivity while maintaining high purity (>=95%) and high recovery (>=90%). It was found that high purity and high recovery are obtained only when the process is isothermal. Thermal fronts propagating through the column impact the process performance. We show that the process cannot achieve recovery targets, i.e., >=90%, unless heat is removed from the system rapidly. The lowest achievable specific steam consumption is ≈45 kg steam/kg CO2 cap and highest achievable productivity is ≈ 0.1 mol CO2/m3 ads/s in an isothermal scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".